Data integration is an important part of a vendor's middleware portfolio in an enterprise system. Data integration consolidates data from multiple database systems into a single database system while ensuring data validation and enrichment of the data. However, working on large quantities of data with a legacy source code provides challenges, such as bottlenecks near a connection to the single database system. A legacy source code is an original written code that is difficult to modify without breaking other logic in the code. Because the data is primarily consolidated from different database systems, these database systems tend to work on portions of the data that logically ties them to one another. Such data is logically tied to one another due to relationship between the data from different database systems and being linked to same application programs.
Current art utilizes a caching mechanism to overcome the challenges presented when consolidating of large quantities of data. Caching provides a storage space to temporarily store data for future data operations. In the situation when many database systems are integrated, current caching mechanisms do not sufficiently solve the problem because these large quantities of data are being used by different parts of the integrated system. Also, the size of the cache in the current caching mechanism limits the amount of data for storage.